Add model card

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+metrics:
+- accuracy
+model-index:
+- name: xtremedistil-l6-h384-emotion
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotion
+      type: emotion
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.928
+---
+# xtremedistil-l6-h384-emotion
+This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the emotion dataset.
+It achieves the following results on the evaluation set:
+- Accuracy: 0.928
+
+This model can be quantized to int8 and retain accuracy 
+- Accuracy 0.912
+
+<pre>
+import transformers
+import transformers.convert_graph_to_onnx as onnx_convert
+from pathlib import Path
+
+pipeline = transformers.pipeline("text-classification",model=model,tokenizer=tokenizer)
+onnx_convert.convert_pytorch(pipeline, opset=11, output=Path("xtremedistil-l6-h384-emotion.onnx"), use_external_format=False)
+from onnxruntime.quantization import quantize_dynamic, QuantType
+quantize_dynamic("xtremedistil-l6-h384-emotion.onnx", "xtremedistil-l6-h384-emotion-int8.onnx", 
+                 weight_type=QuantType.QUInt8)
+</pre>
+
+
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 128
+- eval_batch_size: 8
+- seed: 42
+- num_epochs: 14
+### Training results
+<pre>
+Epoch	Training Loss	Validation Loss	Accuracy
+1	No log	0.960511	0.689000
+2	No log	0.620671	0.824000
+3	No log	0.435741	0.880000
+4	0.797900	0.341771	0.896000
+5	0.797900	0.294780	0.916000
+6	0.797900	0.250572	0.918000
+7	0.797900	0.232976	0.924000
+8	0.277300	0.216347	0.924000
+9	0.277300	0.202306	0.930500
+10	0.277300	0.192530	0.930000
+11	0.277300	0.192500	0.926500
+12	0.181700	0.187347	0.928500
+13	0.181700	0.185896	0.929500
+14	0.181700	0.185154	0.928000
+</pre>